The Landscape of Unfolding with Machine Learning
This provides an incremental toolkit for physicists to enhance measurements of the Standard Model and potentially detect new phenomena.
The paper tackles the problem of data unfolding in particle physics by evaluating a set of machine learning-based methods, finding that all techniques accurately reproduce particle-level spectra across complex observables.
Recent innovations from machine learning allow for data unfolding, without binning and including correlations across many dimensions. We describe a set of known, upgraded, and new methods for ML-based unfolding. The performance of these approaches are evaluated on the same two datasets. We find that all techniques are capable of accurately reproducing the particle-level spectra across complex observables. Given that these approaches are conceptually diverse, they offer an exciting toolkit for a new class of measurements that can probe the Standard Model with an unprecedented level of detail and may enable sensitivity to new phenomena.